Ambient spatial intelligence

Ambient spatial intelligence (AmSI) is concerned with embedding into built and natural environments the intelligence to monitor geographical occurrences and respond to spatiotemporal queries. Consider this somewhat futuristic example:

An Australian site managed for revegetation under a conservation contract.

Following an online auction organized through a local government web site, a conservation contract to reduce nitrogen leaching on a small parcel of private land is won by its owner, Charlie. Two days later a small box of 2000 tiny geosensor nodes arrives by express post. The nodes are pre-configured and programmed with the capability to self-localize and monitor a range of relevant environmental parameters, including temperature, soil moisture, and optical sensors. Charlie distributes the nodes by “sowing” small handfuls of sensor nodes around the site. The nodes activate, organize themselves into an ad hoc network, and localize themselves using a combination of ultrasound range finding and low-power GPS. Over the following three years, the network monitors the environmental changes that result from Charlie’s new management regime, involving the construction of new wetlands on the site. By monitoring the spatial changes that occur, including the emergence of the nitrogen “hot-spots,” and the location, area, and connectivity of new wetlands, the system is able to ensure that the conditions of the conservation contract are being met. Monthly progress summaries are automatically relayed to the government contract manager; Charlie can also monitor changes while in the field using a special application on her smart phone.

There many practical and theoretical hurdles to overcome before applications such as that described above become more than just flights of fancy. However, the example highlights the important of “spatial” technologies and intelligence in overcoming these hurdles, including the importance of: monitoring where and when changes occur; integrating information from multiple sources with space and time as the common framework; reasoning even in the presence of imperfect data; and supporting human interaction with complex spatiotemporal data.